class_docstr: Compute density estimation for each point
  
  {usage}
  
  Parameters
  ----------
  {common_parameters}
  package : Literal["statsmodels", "scipy", "sklearn"], default="statsmodels"
      Package whose kernel density estimation to use.
  kde_params : dict, default=None
      Keyword arguments to pass on to the kde class.
  
  See Also
  --------
  statsmodels.nonparametric.kde.KDEMultivariate
  scipy.stats.gaussian_kde
  sklearn.neighbors.KernelDensity
code: def setup_params(self, data):
      params = self.params.copy()
      if params["kde_params"] is None:
          params["kde_params"] = {}
  
      kde_params = params["kde_params"]
      if params["package"] == "statsmodels":
          params["package"] = "statsmodels-m"
          if "var_type" not in kde_params:
              x_type = get_var_type(data["x"])
              y_type = get_var_type(data["y"])
              kde_params["var_type"] = f"{x_type}{y_type}"
  
      return params
  
file_path: plotnine/plotnine/stats/stat_pointdensity.py
imports: import numpy
  import pandas
  from ..doctools import document
  from ..mapping.evaluation import after_stat
  from .density import get_var_type, kde
  from .stat import stat
label_desc: Use image node_id 1 for calling the stat_pointdensity obj's underlying member method code with example usage: obj.setup_params(data) and returns: params